Convergence of Comorbidity and COVID-19 Infection to Fatality: An investigation based on health assessment and vaccination among older adults in Kerala, India

  1. Sindhu Joseph
  2. Jijo Pulickiyil Ulahannan
  3. Parvathy A J

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Abstract

OBJECTIVE: To investigate the impact of age, comorbidity, and vaccination in the fatality of older COVID-19 patients in the state of Kerala, India. METHODS: A cross sectional study, adopting a mixed method approach was used and conducted among the older population in Kerala. To study the health profile of study participants 405 older people were surveyed and 102 people were interviewed in-depth at their households between June to November 2020. The results of the study were triangulated with elderly COVID-19 fatality data available from the citizen-science dashboards of the research team and Department of Health, Kerala. Vaccination data was retrieved from the Co-WIN government website (cowin.gov.in) to study its impact. The data was analyzed using the IBM SPSS version 22.0. RESULTS: Age is a predictor of COVID-19 fatality. Diabetes, hypertension, CAD, CKD and COPD are the significant predictors of elderly COVID-19 fatality in Kerala. The current comorbidity profile of the total older population matches with the comorbidities of the COVID-19 elderly death cases. CFR and IFR have declined even when the CMR is high in the second wave of COVID-19 with more deaths. This is attributable to vaccination even though there exists a lesser chance for breakthrough infection. CONCLUSIONS: Age and comorbidities can predict potential fatality among older COVID-19 patients. Timely and accurate health data and better knowledge of high-risk factors such as comorbidity can easily guide the healthcare system and authorities to efficient prevention and treatment methodologies. Knowledge on prevailing NCDs can drive early preparedness before it converges with an epidemic like the present zoonotic disease. Vaccination is an effective tool in preventing infection compared to the unvaccinated even though the chance for breakthrough infection is there, particularly, in people with comorbidities.

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  1. Version published to 10.24083/apjhm.v17i1.1191
  2. ScreenIT

    SciScore for 10.1101/2021.01.06.20249030: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementConsent: ” Before the qualitative and quantitative data collection, the participants were briefed about the study’s purpose, and informed consent was obtained.
    RandomizationFrom each of the zones mentioned above, three districts had been selected randomly, and hence Kasaragod, Wayanad and Malappuram (North Zone), Kottayam, Idukki, and Ernakulam (Central Zone) and Kollam, Thiruvananthapuram, and Alappuzha (Southern Zone) districts were selected at random.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The data were analyzed with SPSS 2.0.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    The study has its limitations. Firstly, this study was carried out among the elderly in Kerala, and therefore, the results may not be suitable in an international context. Secondly, getting authentic and correct primary data limited the study, particularly in calculating CFR, which is an estimate and may not be accurate. Thirdly, for the COVID-19, as in the case of previous infectious diseases, the accurate level of transmission is often underestimated as a considerable proportion of the infected population goes undetected for infection due to asymptomatic or mild symptomatic cases failing to test for the infection.72,73 Fourthly, there will be vulnerable segments that are either neglected or under-served and under-detected who are likely to keep themselves away from healthcare access for testing and treatment.74

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2021.01.06.20249030 on medRxiv